






'''simple docstring'''



from __future__ import annotations

SCREAMING_SNAKE_CASE							=						list[tuple[int, int]]

SCREAMING_SNAKE_CASE							=						[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

SCREAMING_SNAKE_CASE							=						([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right
class UpperCAmelCase_       :




      """simple docstring"""




      def __init__(			self      :		Optional[Any]  ,   UpperCAmelCase      :		int  ,   UpperCAmelCase      :		int  ,   UpperCAmelCase      :		int  ,   UpperCAmelCase      :		int  ,   UpperCAmelCase      :		float  ,   UpperCAmelCase      :		Node | None  ,   )	->	List[str]:


       '''simple docstring'''

       lowercase       : Dict						=pos_x
       lowercase       : Any						=pos_y
       lowercase       : Optional[Any]						=(pos_y, pos_x)
       lowercase       : Tuple						=goal_x
       lowercase       : Union[str, Any]						=goal_y
       lowercase       : Union[str, Any]						=g_cost
       lowercase       : str						=parent
       lowercase       : List[Any]						=self.calculate_heuristic()






      def 					A__   (			self      :		int		)	->	float:


       '''simple docstring'''

       lowercase       : List[str]						=abs(self.pos_x - self.goal_x		)
       lowercase       : List[Any]						=abs(self.pos_y - self.goal_y		)
       return dx + dy






      def __lt__(			self      :		Tuple  ,   UpperCAmelCase      :		Tuple		)	->	bool:


       '''simple docstring'''

       return self.f_cost < other.f_cost





class UpperCAmelCase_       :




      """simple docstring"""




      def __init__(			self      :		Tuple  ,   UpperCAmelCase      :		tuple[int, int]  ,   UpperCAmelCase      :		tuple[int, int]		)	->	Optional[Any]:


       '''simple docstring'''

       lowercase       : Tuple						=Node(start[1]  ,   start[0]  ,   goal[1]  ,   goal[0]  ,   0  ,   UpperCAmelCase		)
       lowercase       : int						=Node(goal[1]  ,   goal[0]  ,   goal[1]  ,   goal[0]  ,   9_9999  ,   UpperCAmelCase		)

       lowercase       : str						=[self.start]
       lowercase       : list[Node]						=[]

       lowercase       : Tuple						=False






      def 					A__   (			self      :		Union[str, Any]		)	->	Path | None:


       '''simple docstring'''

       while self.open_nodes:
        # Open Nodes are sorted using __lt__
        self.open_nodes.sort()
        lowercase       : Union[str, Any]						=self.open_nodes.pop(0		)

        if current_node.pos == self.target.pos:
         lowercase       : List[str]						=True
         return self.retrace_path(UpperCAmelCase		)

        self.closed_nodes.append(UpperCAmelCase		)
        lowercase       : Dict						=self.get_successors(UpperCAmelCase		)

        for child_node in successors:
         if child_node in self.closed_nodes:
          continue

         if child_node not in self.open_nodes:
          self.open_nodes.append(UpperCAmelCase		)
         else:
          # retrieve the best current path
          lowercase       : Any						=self.open_nodes.pop(self.open_nodes.index(UpperCAmelCase		)		)

          if child_node.g_cost < better_node.g_cost:
           self.open_nodes.append(UpperCAmelCase		)
          else:
           self.open_nodes.append(UpperCAmelCase		)

       if not self.reached:
        return [self.start.pos]
       return None






      def 					A__   (			self      :		Optional[int]  ,   UpperCAmelCase      :		Node		)	->	list[Node]:


       '''simple docstring'''

       lowercase       : int						=[]
       for action in delta:
        lowercase       : List[Any]						=parent.pos_x + action[1]
        lowercase       : Optional[Any]						=parent.pos_y + action[0]

        if not (0 <= pos_x <= len(grid[0]		) - 1 and 0 <= pos_y <= len(UpperCAmelCase		) - 1):
         continue

        if grid[pos_y][pos_x] != 0:
         continue

        successors.append(
            Node(
                UpperCAmelCase  ,   UpperCAmelCase  ,   self.target.pos_y  ,   self.target.pos_x  ,   parent.g_cost + 1  ,   UpperCAmelCase  ,   )		)
       return successors






      def 					A__   (			self      :		int  ,   UpperCAmelCase      :		Node | None		)	->	Path:


       '''simple docstring'''

       lowercase       : Optional[Any]						=node
       lowercase       : int						=[]
       while current_node is not None:
        path.append((current_node.pos_y, current_node.pos_x)		)
        lowercase       : Any						=current_node.parent
       path.reverse()
       return path


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE							=						(0, 0)
      SCREAMING_SNAKE_CASE							=						(len(grid) - 1, len(grid[0]) - 1)
      for elem in grid:
            print(elem)

      print('------')

      SCREAMING_SNAKE_CASE							=						GreedyBestFirst(init, goal)
      SCREAMING_SNAKE_CASE							=						greedy_bf.search()
      if path:
            for pos_x, pos_y in path:
                  SCREAMING_SNAKE_CASE							=						2

            for elem in grid:
                  print(elem)









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_					=   {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   ["FNetTokenizer"]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   ["FNetTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "FNetForMaskedLM",
      "FNetForMultipleChoice",
      "FNetForNextSentencePrediction",
      "FNetForPreTraining",
      "FNetForQuestionAnswering",
      "FNetForSequenceClassification",
      "FNetForTokenClassification",
      "FNetLayer",
      "FNetModel",
      "FNetPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_fnet import FNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_fnet_fast import FNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_fnet import (
        FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        FNetForMaskedLM,
        FNetForMultipleChoice,
        FNetForNextSentencePrediction,
        FNetForPreTraining,
        FNetForQuestionAnswering,
        FNetForSequenceClassification,
        FNetForTokenClassification,
        FNetLayer,
        FNetModel,
        FNetPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



